Radiolabelling of nanomaterials for medical imaging and therapy
Bibliographic record
Abstract
Nanomaterials offer unique physical, chemical and biological properties of interest for medical imaging and therapy. Over the last two decades, there has been an increasing effort to translate nanomaterial-based medicinal products (so-called nanomedicines) into clinical practice and, although multiple nanoparticle-based formulations are clinically available, there is still a disparity between the number of pre-clinical products and those that reach clinical approval. To facilitate the efficient clinical translation of nanomedicinal-drugs, it is important to study their whole-body biodistribution and pharmacokinetics from the early stages of their development. Integrating this knowledge with that of their therapeutic profile and/or toxicity should provide a powerful combination to efficiently inform nanomedicine trials and allow early selection of the most promising candidates. In this context, radiolabelling nanomaterials allows whole-body and non-invasive in vivo tracking by the sensitive clinical imaging techniques positron emission tomography (PET), and single photon emission computed tomography (SPECT). Furthermore, certain radionuclides with specific nuclear emissions can elicit therapeutic effects by themselves, leading to radionuclide-based therapy. To ensure robust information during the development of nanomaterials for PET/SPECT imaging and/or radionuclide therapy, selection of the most appropriate radiolabelling method and knowledge of its limitations are critical. Different radiolabelling strategies are available depending on the type of material, the radionuclide and/or the final application. In this review we describe the different radiolabelling strategies currently available, with a critical vision over their advantages and disadvantages. The final aim is to review the most relevant and up-to-date knowledge available in this field, and support the efficient clinical translation of future nanomedicinal products for in vivo imaging and/or therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".